An artist's caricature of a face catches one's attention because the artistically exaggerated features impart a comic, grotesque, literary, political, or editorial quality. A worthy caricature emphasizes facial features that make the caricatured person different from everyone else. Hence, the caricatured person is still recognizable yet the exaggerated features make the caricature interesting for viewers.
“Exaggeration” as used herein, means a change in an aspect of a facial image. A usual connotation of the term often implies an increase or an enlargement in something. With regard to the art of caricature, however, an exaggeration can also be a decrease or a diminution of some facial aspect or dimension. In fact, as used herein, exaggeration often means a furthering of some noticeable facial aspect (that distinguishes a person from other people) in the same direction that makes the facial aspect stand out in the first place. So if a person's eyes seem too close together, a good caricature exaggerates this feature by moving the eyes even closer together—but the distance between the eyes decreases: hardly an exaggeration in the conventional sense of the word.
Recently, a number of non-photorealistic rendering algorithms have been proposed to allow computers to draw sketches, engravings, oil paintings, and variously styled line drawings. For example, algorithms for sketches are presented in: H. Chen, Y. Q. Xu, H. Y. Shum, S. C. Zhu, and N. N. Zheng, “Example-based facial sketch generation with non-parametric sampling,” ICCV 2001, 2001. Algorithms for engravings are presented in: V. Ostromoukhov, “Digital facial engraving,” Proceedings of SIGGRAPH 1999, 1999. Algorithms for oil paintings are presented in: A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin, “Image analogies,” Proceedings of SIGGRAPH 2001, Computer Graphics Proceedings, Annual Conference Series, 2001. Algorithms for variously styled line-drawings are presented in: W. T. Freeman, J. B. Tenenbaum, and E. Pasztor, “An example-based approach to style translation for line drawings,” Technical Report TR99-11, 1999; and H. Koshimizu et al., “On kansei facial processing for computerized facial caricaturing system picasso,” IEEE International Conference on Systems, Man, and Cybernetics, vol. 6, 1999, (the “Koshimizu reference”). These conventional systems focus on the painting style or stroke style of the image but not on the exaggeration of faces.
Some tools have been introduced to aid users in creating caricatures (e.g., B. Gooch, E. Reinhard, and A. Gooch, “Perception-driven black-and-white drawings and caricatures,” Technical Report UUCS-02-002, 2002; and E. Akleman, J. Palmer, and R. Logan, “Making extreme caricatures with a new interactive 2D deformation technique with simplicial complexes,” Proceedings of Visual 2001, 2000) but these tools do not instruct users how to perform exaggeration. While experienced caricaturists may be able to generate interesting caricatures with the above-cited tools, inexperienced users may have difficulty determining how to exaggerate a face.
Some conventional systems try to automatically generate caricatures using a computer. The most common way to attempt exaggeration using a computer employs finding differences in facial features (eye, nose, mouth, chin, etc.) between a face to be caricatured and an average face and then enlarging these facial features. This approach has been employed in the work of S. E. Brennan, “Caricature generator: The dynamic exaggeration of faces by computer,” Leonardo, 18(3): 170-178, 1985; in the work of T. Valentine, “A unified account of the effects of distinctiveness, inversion and race in face recognition,” The Quarterly Journal of Experimental Psychology, 43(2): 161-204, 1991; and in the Koshimizu reference, cited above. These conventional systems can provide users with some information about how to perform exaggeration and can allow for adjustment of the exaggeration. But as shown in FIG. 1, these conventional systems are limited to exaggerating only individual features of a face, such as the eyes 102, 102′, 102″ or the mouth 104, 104′, 104.″ In response to a user adjusting the degree of exaggeration, these conventional systems have no mechanism for maintaining facial integrity. For example, sometimes if the distance between two eyes has been enlarged too far, the eyes may extend beyond the outside contour of the face.
A set of pre-designed exaggeration templates is used to generate caricatures in the work of H. Chen, N. N. Zheng, L. Liang, Y. Li, Y. Q. Xu, and H. Y. Shum, “Pictoon: A personalized image-based cartoon system,” ACM Multimedia 2002, 2002. The templates allow facial integrity to be maintained but users have to choose the template to be used for performing exaggeration and the degree of exaggeration is fixed.
In one reference, a caricature training database is used and some exaggeration prototypes are learned from the database using principle component analysis (“PCA”) in the work of L. Liang, H. Chen, Y. Q. Xu, and H. Y. Shum, “Example-based caricature generation with exaggeration,” IEEE Pacific Graphics 2002, 2002. When a new facial image is presented to this conventional system, a particular prototype is chosen automatically and the magnitude of the exaggeration is discovered through linear regression. This system informs a user how to perform exaggeration and determines the magnitude of exaggeration automatically, but users cannot adjust the magnitude of exaggeration applied. Additionally, this conventional system has no mechanism to maintain facial integrity and suffers the additional limitation that its training database is partitioned according to learned exaggeration prototypes, so if there are only a few training samples for each prototype then instability is likely.